Visual inference for IoT systems : a practical approach / Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez.
Velasco-Montero, Delia, author.; Fernández-Berni, Jorge, author.; Rodríguez-Vázquez, Angel, author.
2022
TA1634
Available Online

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Title Visual inference for IoT systems : a practical approach / Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez.
Author Velasco-Montero, Delia, author.
ISBN 9783030909031 (electronic bk.)
3030909034 (electronic bk.)
3030909026
9783030909024
DOI https://doi.org/10.1007/978-3-030-90903-1
Imprint Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-3-030-90903-1 doi
Call Number TA1634
Dewey Decimal Classification 006.3/7
Summary This book presents a systematic approach to the implementation of Internet of Things (IoT) devices achieving visual inference through deep neural networks. Practical aspects are covered, with a focus on providing guidelines to optimally select hardware and software components as well as network architectures according to prescribed application requirements. The monograph includes a remarkable set of experimental results and functional procedures supporting the theoretical concepts and methodologies introduced. A case study on animal recognition based on smart camera traps is also presented and thoroughly analyzed. In this case study, different system alternatives are explored and a particular realization is completely developed. Illustrations, numerous plots from simulations and experiments, and supporting information in the form of charts and tables make Visual Inference and IoT Systems: A Practical Approach a clear and detailed guide to the topic. It will be of interest to researchers, industrial practitioners, and graduate students in the fields of computer vision and IoT.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed February 9, 2022).
Added Author Fernández-Berni, Jorge, author.
Rodríguez-Vázquez, Angel, author.
Available in Other Form Print version: 9783030909024
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Record Appears in Online Resources > Ebooks
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Introduction
Embedded Vision for the Internet of the Things: State-of-the-Art
Hardware, Software, and Network Models for Deep-Learning Vision: A Survey
Optimal Selection of Software and Models for Visual Interference
Relevant Hardware Metrics for Performance Evaluation
Prediction of Visual Interference Performance
A Case Study: Remote Animal Recognition.

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